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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '/content/' # This can be any directory you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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        {
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1x6oYFlVfYY"
      },
      "source": [
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ULXRurzVgjD"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUhXKInOViDg"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5qu0HNtVjvs"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uV3YsRUVqcD"
      },
      "source": [
        "def get_data():     \n",
        "    train = FMNISTDataset(tr_images, tr_targets)     \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)     \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7lhpYHPVr_b"
      },
      "source": [
        "def val_loss(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VindwEyWD-N"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpDI95dnWFSG",
        "outputId": "600f9003-dc4a-40e3-8c0f-66ede8783bbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a46qfaZwbsd-",
        "outputId": "42aa6305-1299-472e-8262-74cc6b59e711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "#ix = np.random.randint(len(tr_images))\n",
        "ix = 24300\n",
        "plt.imshow(tr_images[ix], cmap='gray')\n",
        "plt.title(fmnist.classes[tr_targets[ix]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Trouser')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKEOfcdibsgc"
      },
      "source": [
        "img = tr_images[ix]/255.\n",
        "img = img.view(28*28)\n",
        "img = img.to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9FBQQbcb1q-",
        "outputId": "1fe3d97f-ef09-4a15-fab1-622f4535f18b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "np_output = model(img).cpu().detach().numpy()\n",
        "np.exp(np_output)/np.sum(np.exp(np_output))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.4361714e-06, 9.9999738e-01, 8.3687448e-09, 2.2200647e-08,\n",
              "       5.7493144e-10, 8.5185324e-14, 1.6882856e-07, 7.0115940e-21,\n",
              "       3.0295655e-12, 1.3271068e-13], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzxkVp_vb5nw"
      },
      "source": [
        "Translation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4qSbj_JhEPQ",
        "outputId": "188d478c-4f9d-4e54-90ad-b1d43c133176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tr_targets[ix]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kENvOeLeb9T8",
        "outputId": "718eb5e8-d854-4f71-cc25-7b703f3d9c39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "preds = []\n",
        "for px in range(-5,6):\n",
        "  img = tr_images[ix]/255.\n",
        "  img = img.view(28, 28)\n",
        "  #img2 = np.zeros((28,28))\n",
        "  img2 = np.roll(img, px, axis=1)\n",
        "  plt.imshow(img2)\n",
        "  plt.show()\n",
        "  img3 = torch.Tensor(img2).view(28*28).to(device)\n",
        "  np_output = model(img3).cpu().detach().numpy()\n",
        "  preds.append(np.exp(np_output)/np.sum(np.exp(np_output)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YXf7hiUi5zObd+E8BuBXALpI7C9fdA+AWkhsxNR3XC+DzFRnhbKUcwlqqf+/dkJh98Xd+5m57JOe/fGmiv6xyKbrm/8rNFzf4h+e2Z0+6+ZpGf+oNmnqrG7N5N/55AJwhqtmcuoicP32CTiQIlV0kCJVdJAiVXSQIlV0kCJVdJIg5cyrpVJxp9nAa85c2XvF3ydvf/rWb3W0PHV3m5mvvn3BzYE9Knuze7Z9180ev+babL2gYdfM7fn67m6/BTjeX6tGeXSQIlV0kCJVdJAiVXSQIlV0kCJVdJAiVXSQIWsr8cllvjDwGYPp5l5cBeKtqAzg/9Tq2eh0XoLEVq5xjW2lmF84UVLXs77lxssfMumo2AEe9jq1exwVobMWq1tj0NF4kCJVdJIhal31zjW/fU69jq9dxARpbsaoytpq+ZheR6qn1nl1EqkRlFwmiJmUneTXJX5I8QPLuWowhCclekrtI7iTZU+OxPERyiOTuade1kdxGcn/hq78edHXHdi/J/sJ9t5PktTUaWyfJZ0m+RnIPyS8Vrq/pfeeMqyr3W9Vfs5PMANgH4I8A9AF4CcAtZvZaVQeSgGQvgC4zq/kHMEj+PoBhAN81sw2F674G4LiZ3Vd4oFxiZnfVydjuBTBc62W8C6sVdUxfZhzA9QBuRw3vO2dcN6EK91st9uyXAjhgZofMbBzA9wFcV4Nx1D0zew7A8XddfR2ALYXLWzD1n6XqEsZWF8xswMx2FC6fBnBumfGa3nfOuKqiFmVfAeDItO/7UF/rvRuAn5B8mWR3rQczg3YzGyhcHgTQXsvBzCB1Ge9qetcy43Vz3xWz/Hmp9Abde11uZh8HcA2ALxSertYlm3oNVk9zp7NaxrtaZlhm/Ndqed8Vu/x5qWpR9n4AndO+f1/hurpgZv2Fr0MAnkT9LUV99NwKuoWvaSsrVk09LeM90zLjqIP7rpbLn9ei7C8BWEdyNclGADcD2FqDcbwHydbCGycg2QrgKtTfUtRbAdxWuHwbgKdqOJbfUC/LeCctM44a33c1X/7czKr+B8C1mHpH/iCAv6nFGBLGtQbAK4U/e2o9NgCPYepp3QSm3tu4A8BSANsB7AfwDIC2OhrbowB2AXgVU8XqqNHYLsfUU/RXAews/Lm21vedM66q3G/6uKxIEHqDTiQIlV0kCJVdJAiVXSQIlV0kCJVdJAiVXSSI/wMr6yjE1OGoXAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TtndO+7YbAOxKvz0RSUsxr8Z/BsCtAHaS3FHYdg+AW0iux9R0XB+AL1akw3qRcBprOf6jb51b//Lv/cKtH86Fnx41019SuVzdc3/j1hc2hE/P7ciccMeuavKn3qCpt1kp5tX45wFwhtL7dk5d5P1I76ATiYTCLhIJhV0kEgq7SCQUdpFIKOwikdClpNPAmWYmpzF/WeNl/+CPv/0bN7v1g0eWBGur78+6Y4HdCXXfvds+79YfvebBYG1ew5g79o5f3u7WV2GHW5dz6cguEgmFXSQSCrtIJBR2kUgo7CKRUNhFIqGwi0SCljAHnOrOyKMApl8XeQmAt6rWwOzUa2/12heg3kqVZm/LzezCmQpVDfu7dk72mll3zRpw1Gtv9doXoN5KVa3e9DBeJBIKu0gkah32jTXev6dee6vXvgD1Vqqq9FbT5+wiUj21PrKLSJUo7CKRqEnYSV5N8tck95O8uxY9hJDsI7mT5A6SvTXuZRPJYZK7pm1rJ7mV5L7C5/B6zdXv7V6SA4X7bgfJa2vUWxfJZ0m+RnI3ya8Uttf0vnP6qsr9VvXn7CQbAewF8CcA+gG8BOAWM3utqo0EkOwD0G1mNX8DBsk/BDAC4Ptmtq6w7RsAjpnZfYU/lIvM7K466e1eACO1Xsa7sFpR5/RlxgFcD+B21PC+c/q6CVW432pxZL8UwH4zO2hmEwB+COC6GvRR98zsOQDHztt8HYDNhdubMfWfpeoCvdUFMxs0s+2F26cAnF1mvKb3ndNXVdQi7MsAHJ72dT/qa713A/Azki+T7Kl1MzPoMLPBwu0hAB21bGYGict4V9N5y4zXzX1XyvLn5dILdO92uZl9EsA1AL5UeLhal2zqOVg9zZ0WtYx3tcywzPhv1fK+K3X583LVIuwDALqmff2Bwra6YGYDhc/DAJ5E/S1FfeTsCrqFz0mrH1ZNPS3jPdMy46iD+66Wy5/XIuwvAVhDciXJJgA3A9hSgz7ehWRb4YUTkGwDcBXqbynqLQBuK9y+DcBTNezlHPWyjHdomXHU+L6r+fLnZlb1DwDXYuoV+QMA/q4WPQT6WgXglcLH7lr3BuAxTD2sy2LqtY07ACwGsA3APgDPAGivo94eBbATwKuYClZnjXq7HFMP0V8FsKPwcW2t7zunr6rcb3q7rEgk9AKdSCQUdpFIKOwikVDYRSKhsItEQmEXiYTCLhKJ/weFxSjELXE2HAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXFrgJ9uf8mp",
        "outputId": "2846c10e-f6ee-4b07-a477-964efe241cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "import seaborn as sns\n",
        "fig, ax = plt.subplots(1,1, figsize=(12,10))\n",
        "plt.title('Probability of each class for various translations')\n",
        "sns.heatmap(np.array(preds), annot=True, ax=ax, fmt='.2f', xticklabels=fmnist.classes, yticklabels=[str(i)+str(' pixels') for i in range(-5,6)], cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe8819940f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugxiRTlpgVas"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}